In general, accurate recognition of lanes is an essential element for the implementation of an advanced driver assistance systems (ADAS) and the realization of autonomous vehicles. In most cases, the road image is obtained by mounting the vision sensor based camera on the front of the vehicle, and the lane is detected through the image processing process. The results of the lane recognition can be used for Lane Departure Warning System (LDWS), Lane Keeping Assist System, etc.
More research has been carried out for accurate and robust recognition of the lane that provides this important information. The most representative method for lane detection is a method of extracting straight line components of a certain length or more in the image by using Hough transform after extracting the edges of an input image.
However, in the case of Hough transform, various attempts have been proposed to reduce an area to be computed as much as possible because of the large computational burden. In other words, by using the brightness characteristics of the lane area or by analyzing the color information of the lane, it is proposed to improve the processing time by setting it as the region of interest (ROI) and searching the limited range.
The results of the lane recognition are derived from the application of the spline, the least squares method, or the RANSAC (RANdom SAmple Consensus) method.
The result of the lane recognition is used to derive the lane equations from the third or higher-order equations through the method by using the application of the spline or the least squares method, or RANSAC, etc.
However, in spite of efforts to reduce the computation burden for real-time processing through a lot of conventional studies, due to the sensitivity to the environment and lighting changes that an image sensor contains, additional post-processing algorithms are required, which makes real-time processing of lane recognition more difficult in an embedded environment.